Complex events, such as those encountered within healthcare enterprises, may be defined as sequences of events in time that are dynamically coupled together through explicit or implied relationships and interdependencies. The implied interdependencies may be subtle and may exist through multiple layers of indirection. Evaluating automated rules upon such complex event conditions is generally extremely challenging. When the combined events and time sequences making up the complex events are subtly related or when their relationships depend upon multiple layers of indirection, the challenges increase significantly, effectively becoming intractable to traditional approaches.
Data associated with complex events may be weakly structured or even unstructured. Such datasets often attempt to aggregate frequently changing information from many diverse sources having different structures and formats. Operating rules associated with such data may also be complex and highly dynamic.
There are numerous challenges to implementing intelligent rule systems to handle complex events that may be made up of loosely interrelated time sequences of events that each may involve complex datasets of information and their associated production rules.
There is a need in the art for complex event handling technology that can safely and efficiently support very large, unstructured datasets of interrelated sequences of occurrences, each of which may involve numerous parameters, the values and relationships of which may frequently change in real time. Such solutions would be particularly applicable in large, information-driven enterprises such as healthcare facilities or systems thereof.